An enterprise's day-to-day transactions can be described by three different types of data: operational data, analytical data, and master data. Operational data describes an enterprise's operations and is typically provided and used by operational, or transactional, systems. By contrast, analytical data describes an enterprise's decision making and is typically provided by and used by analytical systems. Master data describes a consolidation of operational data and analytical data to produce a single, reliable version of data representative of the enterprise.
One important objective for enterprises is to maintain an accurate and up-to-date version of master data in a master hub, given that the master data supports the operational and analytical sides of an enterprise. In order to create and maintain master data within a master hub, it is desirable to consolidate and cleanse data records received from the various operational and analytical systems. However, master data quality issues may arise due to incomplete and/or erroneous information within data records received from the various operational and analytical systems. These data quality issues can multiply as the number of operational and analytical systems in an enterprise are increased.
One way to address data quality issues is by using data governance tools to ensure proper handling of data records. Data governance tools are used to monitor data quality at each operational and analytical system and at a master data hub. An enterprise can make use of data governance tools at each system and hub, but this can lead to compartmentalization. Such use of separate tools at each system and hub fails to provide a streamline process by which data is governed (i.e., received, handled, processed, evaluated, corrected, and made viewable) throughout all systems and hubs of an enterprise.
It is therefore desirable to provide a single, complete data governance solution for an enterprise. It is further desirable that the data governance solution allow consistent data governance functionality across all systems and hubs of an enterprise, thereby allowing viewing data flows, and identifying and correcting data quality issues across all systems and hubs.